DCS 530 -- Principles of Artificial Intelligence

Fall 2003

Main Page Contents
About this course

Course People
Matthew Stone
Aynur Dayanik


Tuesday/Thursday 4:30-5:50, Core 301A

Office Hours
MS: Thursday 10-12 (Core 328).
AD: Tuesday 2-4 (Core 333).


  • Tuesday, Dec 16, 4:00 pm
    Final, Hill 254.
    The exam will be 90 minutes.
    Those with another 6pm exam may begin at 3:15pm.

  • Dec 11
    Selected answers to sample problems (no pictures).

  • Dec 4
    Sample problems.

  • Nov 20
    Additional readings will be available presently in the Math library and on electronic reserve.

  • Nov 13
    Homework 3 corrected. Numerical change will make answering 1h easier.

  • Nov 6
    Homework 3 out, due Nov 18.
    Final paper out, due Dec 9.

  • The midterm, Oct 21
    will take place back in Hill 254.

  • Oct 2
    Homework 2 out, due Oct 14.

  • Sep 30
    A reminder to hand in your program in homework 1 electronically by email to mdstone@cs.

  • Sep 11
    Quick clarification on correlation, as used by Paskin to describe the SLAM problem. Correlation can be understood intuitively as the interdependence of variable quantities. It is measured in terms of covariance.
    ...it is difficult to employ the covariance as an absolute measure of dependence because its value depends on the scale of measurement and so it is hard to determine whether a particular covariance is large at first glance. This problem can be elimnated by standardizing its value, using the simple coefficient of linear correlation. The population linear coefficient of correlation, ρ, is related to the covariance and is defined as
    ρ = covariance(X,Y)/(σ(X)σ(Y))
    where σ(X) and σ(Y) are the standard deviations of X and Y respectively. [Mendenhall et al, Math. Stats. with Applications, PWS Kent 1990]

  • Sep 09
    Reminder: Class is moved to Core 301A.
    Homework One out.

  • Sep 02
    Initial revision of this web page.
    Announcement: Who should take this class?

Lecture Schedule, AI Events, Notes

This class will work from an eclectic combination of survey and tutorial papers, research articles, course notes, and other resources. This list will grow as the semester proceeds.